Using a machine learning module to dynamically determine tracks to prestage from storage to cache

ABSTRACT

Provided are a computer program product, system, and method for determining tracks to prestage into cache from a storage. Information is provided related to determining tracks to prestage from the storage to the cache in a stage group of sequential tracks including a trigger track comprising a track number in the stage group at which to start prestaging tracks and Input/Output (I/O) activity information to a machine learning module. A new trigger track in the stage group at which to start prestaging tracks is received from the machine learning module having processed the provided information. The trigger track is set to the new trigger track. Tracks are prestaged in response to processing an access request to the trigger track in the stage group.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a computer program product, system, andmethod for using a machine learning module to dynamically determinetracks to prestage from storage to cache.

2. Description of the Related Art

In a storage system, a storage controller may prestage tracks from thestorage into cache to make available for a sequential process that isaccessing tracks in sequence. In this way, the sequential process may beimmediately provided the requested tracks from cache without having towait for the tracks to be staged into the cache from a further awaystorage or memory device having a slower access profile than the cachedevice. A prestage process is defined by a trigger track indicating atrack number, or track offset, in a stage group of sequential tracks atwhich prestaging starts and a prestage amount of a number of sequentialtracks from the trigger track to prestage. In this way, when thesequential access process accesses the trigger track, the prestageamount of sequential tracks from the trigger track are prestaged.

In an adaptive prestaging algorithm, the trigger track and prestageamount may be adjusted to increase the likelihood of cache hits, i.e., arequested track was previously prestaged into the cache at the time ofthe request to the track. An adaptive prestaging algorithm may increasethe prestage amount if the last track of a stage group is read, up tothe maximum number of tracks in a stage group. If there is a cache miss,i.e., a requested track is not in the cache, then the trigger track maybe reduced to start prestaging earlier in the stage group. If a trackwas prestaged and is about to be demoted before being accessed, such asat the Least Recently Used (LRU) end of a cache LRU list, then thattrack may be added to a Most Recently Used (MRU) end of the cache LRUlist. If the track in cache is about to be demoted after already havingbeen moved to the MRU end after the previous demotion, then the track isdemoted. When the track is accessed, the prestage amount may bedecreased and trigger track may be increased, to prestage fewer tracks.If the prestage trigger track is less than a prestage amount by apredetermined minimum distance, then the trigger track number is reducedto prestage further away from the prestage amount.

There is a need in the art for improved techniques to determine when toprestage tracks and how many tracks to prestage.

SUMMARY

An embodiment may comprise a computer program product, system, andmethod for determining tracks to prestage into cache from a storage.Information is provided related to determining tracks to prestage fromthe storage to the cache in a stage group of sequential tracks includinga trigger track comprising a track number in the stage group at which tostart prestaging tracks and Input/Output (I/o) activity information to amachine learning module. A new trigger track in the stage group at whichto start prestaging tracks is received from the machine learning modulehaving processed the provided information. The trigger track is set tothe new trigger track. Tracks are prestaged in response to processing anaccess request to the trigger track in the stage group.

The described embodiment provide improvements to computer technology fordetermining tracks to prestage by using a machine learning module thatis trained to produce a trigger track and prestage amount values thatoptimize computer performance parameters such as bandwidth, cache hits,and response time based on current operational parameters. The machinelearning module trained for such optimization may then be used todetermine the optimal trigger track and prestage amount based on currentoperational parameters. This allows for continual and dynamic adjustmentof the trigger track and prestage amount as operational parameterschange, such as load on computational resources in the form of allocateddestage and stage tasks, used bandwidth in device adaptors, average timeof a track in cache, etc.

In a further embodiment, a prestage amount indicating a number of tracksto prestage upon processing the trigger track in the stage group isreceived from the machine learning module having processed the providedinformation. The prestaging tracks in response to the processing thetrigger track comprises prestaging the prestage amount of tracks fromthe storage to the cache.

With the above further embodiment, the machine learning module furtherprovides a prestage amount to optimize selection of both prestageparameters, the trigger track and prestage amount.

In a further embodiment, the information related to determining tracksto prestage provided to the machine learning module comprises aplurality of: an I/O speed of a storage device on which prestaged tracksreside; the trigger track; the prestage amount; a current adaptorbandwidth through which data is transferred between the cache and thestorage device; and a number of destage and stage tasks allocated todestage tracks from the cache to the storage and stage tracks from thestorage to the cache.

With the above further embodiment, the machine learning module mayconsider performance parameters such as adaptor bandwidth and number ofdestage and stage tasks allocated for destage and stage operations,respectively. This allows the machine learning module to be trained tooptimize the selection of the trigger track and prestage amount onvarious performance parameters so as to select the trigger track andprestage amount to result in improved performance parameters.

In a further embodiment, the storage unit comprises a Redundant Array ofIndependent Disk (RAID) rank of a plurality of RAID ranks, wherein eachof the RAID ranks is comprised of storage devices.

In a further embodiment, a request to access a track in the cache isprocessed. The information related to determining tracks to prestage isprovided to the machine learning module in response to processing therequest to access the track.

With the above embodiment, the determining the optimized prestageparameters, such as the trigger track and prestage amount is performedwhen processing a request to access a track to adjust the trigger trackand prestage amount to optimize for current operating conditions at atime the track is accessed.

In a further embodiment, a determination is made as to whether theaccessed tack comprises the new trigger track from the machine learningmodule. The prestaging of tracks is performed in response to theaccessed track comprising the new trigger track.

With the above embodiment, the determined optimal trigger track isutilized immediately for a just accessed track to allow the prestagingif the just accessed track happens to be the newly determined optimaltrigger track. This allows for immediate implementation of the optimaltrigger track determined by the machine learning module for the currentaccess to a track.

In an additional embodiment of a computer program product, system, andmethod for determining tracks to prestage into cache from a storage, amachine learning module receives as input information related todetermining tracks to prestage, from the storage to the cache, in astage group of sequential tracks including a trigger track comprising atrack number in the stage group at which to start prestaging tracks andInput/Output (I/O) activity information. The machine learning moduleoutputs at least one of a new trigger track and a new prestage amount oftracks to prestage when processing the trigger track.

At least one of a current trigger track and a current prestage amountare adjusted based on information related to an accessed track in thecache to produce at least one of an adjusted current trigger track andan adjusted current prestage amount. The machine learning module isretrained with the information related to determining tracks to prestageto produce at least one of the adjusted current trigger track and theadjusted current prestage amount. The retrained machine learning moduleis used to produce at least one of the new trigger track and the newprestage amount to use to determine tracks to prestage.

With the above embodiment, the machine learning module is retrainedbased on an adjusted trigger track and prestage amount that are adjustedbased on information related to an accessed track. The machine learningmodule is retrained to produce the adjusted trigger track and prestageamount based on current operational conditions.

In a further embodiment, the information related to the accessed trackcomprises a time margin of error based on a difference of a current timeand a time when the accessed track was prestaged. The adjusting the atleast one of the current trigger track and the current prestage amountcomprises adjusting the at least one of the current trigger track andthe current prestage amount by the time margin of error.

With the above further embodiment, the trigger track and prestage amountare adjusted based on a time margin of error indicating a length of timea track was in the cache to provide an adjustment based on whether theprestaged track was in track for too long or too short of a period oftime, which can result in prestaged tracks being demoted before beingaccessed or too few tracks being prestaged which results in cachemisses.

In a further embodiment, the accessed track comprises a demoted trackselected for demotion from the cache to the storage.

With the above embodiment, the prestage amount and trigger track areadjusted for retraining the machine learning module based on informationrelated to a demoted track to retrain and optimize the machine learningmodule based on information on the current track being demoted.

In a further embodiment, a determination is made as to whether thedemoted track was accessed while in the cache. The adjusting the atleast one of the current trigger track and the current prestage amountcomprises performing at least one of decreasing the current triggertrack and increasing the current prestage amount by a percentage basedon the time margin of error in response to determining that the demotedtrack was accessed while in the cache and performing at least one ofincreasing the current trigger track and decreasing the current prestageamount by a percentage based on the time margin of error in response todetermining that the demoted track was not accessed while in the cache.

With the above embodiment, when the demoted track was accessed while inthe cache, the prestage parameters may be modified to allow more tracksto be prestaged, such as by decreasing the trigger track to triggerprestaging earlier in the stage group and increase the number of tracksprestaged by the time margin based on how long the track was in cache.However, if the demoted track was not accessed while in cache, thenprestaged tracks need to remain longer in cache to be available for afuture access, which can be implemented by increasing the currenttrigger track to cause prestaging later in the stage group anddecreasing the prestage amount to prestage fewer tracks.

In a further embodiment, the information related to the accessed trackcomprises a track position margin of error based on a difference of thecurrent trigger track and a number of the accessed track in the stagegroup. The adjusting the at least one of the current trigger track andthe current prestage amount comprises decreasing the current triggertrack by the track position margin of error to prestage tracks earlierin the stage group.

With the above embodiment, a distance of the accessed track from thetrigger track, or track position margin of error, is used to determinehow to adjust the trigger track and prestage amount, so that the currenttrigger track can be decreased by the track position margin of error tocause more prestaging earlier in the stage group.

In a further embodiment, the accessed track comprises a track subject toan I/O request as part of a sequential access that is not in the cache.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of a computing environment.

FIG. 2 illustrates an embodiment of a processing node.

FIG. 3 illustrates an embodiment of prestage information.

FIG. 4 illustrates an embodiment of Input/Output (I/O) activityinformation.

FIG. 5 illustrates an embodiment of operations to use a machine learningmodule to determine a trigger track and/or prestage amount forprestaging of tracks.

FIG. 6 illustrates an embodiment of operations to retrain the machinelearning module when demoting a track from the cache.

FIG. 7 illustrates an embodiment of operations to retrain the machinelearning module when detecting a cache miss for a requested track.

FIG. 8 illustrates a computing environment in which the components ofFIG. 1 may be implemented.

DETAILED DESCRIPTION

Current adaptive prestaging algorithms do not take into account a devicespeed of the storage device staging tracks and the effect of prestagingon bandwidth availability in the device adaptor between the cache andthe storage, and the effect of bandwidth on operations.

Described embodiments provide improvements to the computer technologyfor prestaging tracks from a storage device into cache by using amachine learning module that is trained to produce a trigger track andprestage amount values that optimize computer performance parameterssuch as bandwidth, cache hits, and response time based on currentoperational parameters. The machine learning module trained for suchoptimization may then be used to determine the optimal trigger track andprestage amount based on current operational parameters. This allows forcontinual and dynamic adjustment of the trigger track and prestageamount as operational parameters change, such as load on computationalresources in the form of allocated destage and stage tasks, usedbandwidth in device adaptors, average time of a track in cache, etc. Inthis way, the prestage trigger track and prestage amount are continuallyadjusted to optimize operational parameters such as cache hits, adaptorbandwidth, response time, etc. for the current operational parameters inthe system.

FIG. 1 illustrates an embodiment of a storage controller 100 including aplurality of independent processing nodes 200 ₁, 200 ₂, such as acentral electronics complex (CEC), that each connect to Input/Output(I/O) bays 102 a, 102 b having device adaptors (DA) 104 ₁, 104 ₂, 104 ₃,104 ₄, also referred to as storage adaptors, and host adaptors (HA)therein, over a bus interface 105, such as a Peripheral ComponentInterconnect Express (PCIe) bus. The processing nodes 200 ₁, 200 ₂ mayalso communicate with each other directly over a link 106, such as aPCIe bus. Host systems, such as host 108, may connect to the storagecontroller 100 through a host adaptor (HA) in the I/O bays 102 a, 102 b.

A storage system 110 includes a plurality of storage devices 112 ₁ . . .112 _(m), in which tracks, logical volumes, ranks of logical contiguousstorage spaces, and storage arrays, such as Redundant Arrays ofIndependent Disks (RAID), may be configured. Each processing node 200 ₁,200 ₂ may be assigned one of the device adaptors in each of the I/O bays104 a, 104 b that connect to the storage array 110 to provide access todata configured in the storage devices 112. Each processing node 200 ₁,200 ₂ has a default configuration to communicate with a device adaptor(DA) 104 ₁, 104 ₂, 104 ₃, 104 ₄ in one of the I/O bays 102 a, 102 b,where the default configuration will assign the different processingnodes 200 ₁, 200 ₂ to device adaptors in the I/O bays 102 a, 102 b.

In the embodiment of FIG. 1 , two redundant processing nodes 200 ₁, 200₂ and two I/O bays 102 a, 102 b are shown. In further embodiments, theremay be more than the number of shown redundant elements 200 ₁, 200 ₂,102 a, 102 b, to provide additional redundancy. Alternatively, there maybe only one processing node in the system.

The storage controller 100 may comprise a storage system, such as theInternational Business Machines Corporation (IBM®) DS8000® and DS8880storage systems, or storage controllers and storage systems from othervendors. (IBM and DS8000 are trademarks of International BusinessMachines Corporation throughout the world).

The storage devices 112 ₁ . . . 112 _(m) in the storage system 110 maycomprise different types or classes of storage devices, such as magnetichard disk drives, magnetic tape storage, solid state storage device(SSD) comprised of solid state electronics, EEPROM (ElectricallyErasable Programmable Read-Only Memory), flash memory, flash disk,Random Access Memory (RAM) drive, storage-class memory (SCM), etc.,Phase Change Memory (PCM), resistive random access memory (RRAM), spintransfer torque memory (STM-RAM), conductive bridging RAM (CBRAM),magnetic hard disk drive, optical disk, tape, etc. Storage arrays mayfurther be configured ranks in the storage devices 112 ₁ . . . 112 _(m),such as Just a Bunch of Disks (JBOD), Direct Access Storage Device(DASD), Redundant Array of Independent Disks (RAID) array,virtualization device, etc. Further, the storage devices 112 ₁ . . . 112_(m) in the storage 110 may comprise heterogeneous storage devices fromdifferent vendors and different types of storage devices, such as afirst type of storage devices, e.g., hard disk drives, that have aslower data transfer rate than a second type of storage devices, e.g.,SSDs.

FIG. 2 illustrates an embodiment of a processing node 200 _(i), such asone of the processing nodes 200 ₁, 200 ₂, including a processor 202,such as one or more processor devices, and a memory 204 having programcode executed by the processor 202. The memory 204 includes an I/Omanager 206 to manage I/O requests from attached hosts 108 to storagearrays configured in the storage devices 112 ₁ . . . 112 _(m), andperform other related operations, such as path selection and errorhandling. The memory 204 further includes a cache 208 to cache tracksfrom the storage devices 112 ₁ . . . 112 _(m) and a non-volatile storage(“NVS”) 210 to provide a backup storage of modified tracks in the cache208 in another processing node 200 _(j). Each processing node 200 ₁, 200₂ may be assigned groups of the storage arrays configured in the storagedevices 112 ₁ . . . 112 _(m).

The memory 204 includes prestage information 300 used to determine whento start prestaging tracks in a stage group of tracks and a prestageamount of a number of tracks to prestage. For instance, tracks may beoperated upon and read as part of a stage group of consecutive tracksand the trigger track indicates a track number in the stage group thatwhen processed causes the prestaged amount of tracks to be prestaged.The memory 204 further includes I/O activity information 400 havinginformation on I/O access.

In embodiments where the host 108 operating system comprises Z SystemsOperating System (Z/OS®), or other similar operating system, an I/Orequest may define the stage group of tracks subject to operations, suchas sequential access operations, by providing a locate record domainthat specifies the subset of tracks subject to the I/O operations, andthe trigger track would be the track at the track number in the locaterecord domain matching the trigger track number.

The memory 204 includes a machine learning module 212 that receives asinput 214 prestage information 300 and I/O activity information 400 andcomputes a trigger track 216 and prestage amount 218. The machinelearning module 212 implements a machine learning technique such asdecision tree learning, association rule learning, artificial neuralnetwork, inductive programming logic, support vector machines, Bayesianmodels, etc. The I/O manager 206 may then use the outputted triggertrack 216 and prestage amount 218 to determine when in a stage group ofsequential tracks to prestage and a prestage amount 218 of tracks toprestage from the trigger track 216. The arrows shown from the input 214to the machine learning modules 212 and to the outputs 216, 218illustrate a flow of data to and from the machine learning module 212and not actual structures in the memory 204.

In one embodiment, the machine learning modules 212 may compriseartificial neural network programs. Each neural network may be trainedusing backward propagation to adjust weights and biases at nodes in ahidden layer to produce the computed trigger track and prestage amount.The machine learning module 212 is trained to produce a trigger track216 and prestage amount 218 to optimize and balance computational goals,such as minimize cache misses and optimize other parameters, such asresponse time and bandwidth in the device adaptors 104 ₁, 104 ₂, 104 ₃,104 ₄ transferring data between the cache 208 and the storage 110. Inbackward propagation used to train a neural network machine learningmodule, margin of errors are determined based on operational parameters,such as a time a demoted track has been in cache and a position of anaccessed track resulting in a cache miss in a stage group with respectto the trigger track, and biases at nodes in the hidden layer areadjusted accordingly to decrease the margins of error in these measuredstorage parameters. Backward propagation may comprise an algorithm forsupervised learning of artificial neural networks using gradientdescent. Given an artificial neural network and an error function, themethod may calculate the gradient of the error function with respect tothe neural network's weights and biases.

Although FIG. 2 shows one machine learning module 212, there may beseparate machine learning module 212 for each RAID rank, or otherstorage unit, configured in the storage 110 or one machine learningmodule for all the ranks, or one or more storage units configured in thestorage 110.

The I/O manager 206 and machine learning module 212 are shown in FIG. 2as program code loaded into the memory 204 and executed by the processor202. Alternatively, some or all of the functions may be implemented inhardware devices in the processing nodes 200 ₁, 200 ₂, such as inApplication Specific Integrated Circuits (ASICs) or executed by separatededicated processors.

The memory 204 and cache 208 may comprise one or more memory devices,such as a Dynamic Random Access Memory (DRAM), a phase change memory(PCM), Magnetoresistive random-access memory (MRAM), Spin TransferTorque (STT)-MRAM, SRAM storage devices, DRAM, a ferroelectricrandom-access memory (FeTRAM), nanowire-based non-volatile memory, and aNon-Volatile Direct In-Line Memory Modules (DIMMs) with byte-addressablewrite-in-place memory, etc. The non-volatile storage (“NVS”) 210 maycomprise a non-volatile storage, such as NAND storage, e.g., flashmemory, Solid State Drive (SSD) storage, non-volatile RAM, etc. Othernon-volatile devices may be used for the non-volatile storage 210, suchas a battery backed-up DIMM.

FIG. 3 illustrates an embodiment of prestage information 300 including atrigger track 302 indicating a track number offset in a stage group ofsequential tracks being processed, such as a locate record domain orother group, at which the I/O manager 206 starts prestaging tracks and aprestage amount 304 of a number of tracks to prestage from the triggertrack 302. The parameters 302 and 304 may be set when outputted from themachine learning module 212 as values 216 and 218.

FIG. 4 illustrates an embodiment of I/O activity information 400gathered by the I/O manager 206 to use as input 214 to the machinelearning module 212 and includes a storage device speed 402 of thestorage devices 112 ₁, 112 ₂ . . . 112 _(m) storing the tracks in thestage group based on a measured operational speed or a speed determinedby the manufacturer and indicated in device specifications; a currentadaptor bandwidth 404 indicating current used and/or available bandwidthin the device adaptors 104 ₁, 104 ₂, 104 ₃, 104 ₄; a current number ofdestage tasks 406 allocated to destage tracks from the cache 208 to thestorage 110; a current number of stage tasks 408 allocated to stagetracks from the storage 110 to the cache 208; and an average time atrack is in the cache 410. In a RAID embodiment, there may be a separateinstance of prestage information 300 and I/O activity information 400for each RAID rank.

FIG. 5 illustrates an embodiment of operations performed by the I/Omanager 206 to process a request to read a track as part of a sequentialaccess to a stage group of tracks. The operations of FIG. 5 may also beperformed after a predetermined number of track accesses to adjust thetrigger track and prestage amount after a number of accesses. Uponreceiving (at block 500) a sequential access request, the I/O manager206 provides (at block 502) the machine learning module 212 as input 214prestage information 300, (current trigger track 302 and prestage amount304) and I/O activity information 400 (e.g., I/O speed of a storagedevice 402 on which the proceed track resides; adaptor bandwidth 404through which data is transferred from the cache 208 to the storagedevice 112, a number of destage tasks 406 allocated to process a destagequeue to destage tracks from the cache 208 to the storage 110; and anumber of stage tasks 408 allocated to process a stage queue to stagetracks from the storage 110 to the cache 208). Other informationconcerning operational and computer performance parameters that could beaffected by increasing or decreasing the number of tracks prestaged intocache 208 may also be considered.

In response to the input 214, the I/O manager 206 receives (at block504) from the machine learning module 212 a new trigger track 216 and/ornew prestage amount 218 and sets (at block 506) the current triggertrack 302 and current prestage amount 304 to the received values 216 and218. If (at block 508) the accessed track comprises the current/newtrigger track 216, such as the accessed track number comprising thetrigger track 216 number, then the I/O manager 206 prestages (at block510) the prestage amount 218 of the consecutive tracks from the triggertrack 216 or current accessed track from the storage 110 into the cache208. Otherwise, if the accessed track does not yet comprise the triggertrack 216, control ends.

With the embodiment of FIG. 5 , the trigger track 302 and/or prestageamount 304 are continually updated after track accesses to optimizeperformance parameters to improve computer operations, such as improvebandwidth, response time, stage and destage operations, etc. This allowscontinual modification of the prestage parameters of trigger track andprestage amount to optimize computer performance.

FIG. 6 illustrates an embodiment of operations performed by the I/Omanager 206 and/or machine learning module 212 to retrain the machinelearning module 212 to improve how it calculates the trigger track 216and prestage amount 218 to optimize computer performance when demoting atrack from the cache. The operations of FIG. 6 may be performed whendemoting each track from the cache 208 or after demoting a predeterminednumber of tracks from the cache 208. Upon demoting (at block 600) atrack from the cache 208, the I/O manager 206 determines (at block 602)whether the track is subject to a sequential access to multiple tracks.If not, control ends. If (at block 602) the track is subject tosequential access, then the I/O manager 206 determines (at block 604) atime margin of error based on a time the demoted track was in cache, andmay be calculated as (current time−time demoted track was prefetched)divided by an average time tracks are in the cache 410. The average timetracks are in cache 410 may be updated whenever a track is demoted fromthe cache 208. If (at block 606) the demoted track was not accessedwhile in the cache 208, then the current trigger track 302 is increased(at block 608) by the time margin of error and/or the prestage amount304 is decreased by the time margin of error to produce an adjustedtrigger track/and or adjusted prestage amount. If the demoted track wasnot accessed, then this means that too many tracks are being prefetchedto overfill the cache to cause prefetched tracks to be demoted beforethe prefetched tracks are accessed. In this way, the operation at block608 causes fewer tracks to be prefetched by increasing the trigger track302 to cause prefetching later in the stage group of tracks and/or bydecreasing the prestage amount 304 to cause fewer tracks to be prestagedwhen the trigger track is processed. Thus, by prestaging fewer tracks,prestaged tracks will remain in cache 208 longer, i.e., not forced outby too many prestages, to be available to be accessed when thesequential process accesses that prefetched track.

If (at block 606) the demoted track was accessed in the cache 208, thenthe current trigger track 302 is decreased (at block 610) by the timemargin of error and/or the prestage amount 304 is increased by the timemargin of error to produce an adjusted trigger track/and or adjustedprestage amount. If the demoted track was accessed before being demoted,then this means that the cache 208 may be able to accommodate moretracks being prefetched. In this way, the operation at block 610 causesmore tracks to be prefetched by decreasing the trigger track 302 tocause prefetching earlier in the stage group of tracks and/or byincreasing the prestage amount 304 to cause more tracks to be prestagedwhen the trigger track is processed. Thus, by prestaging more tracks, asequentially accessed track will more likely be in cache 208 whenrequested because more tracks are being prestaged.

In an embodiment of blocks 608 and 610, the increasing or decreasing bythe time margin of error may comprise multiplying the trigger track andprestage amount by one plus the margin of error to increase ormultiplying by one minus the margin of error to decrease. In alternativeembodiments, the time margin of error may be used in other functions toincrease or decrease the trigger track and destage amount. Inalternative embodiments, the time margin of error may be calculated inadditional ways using the current time and a time the demoted track wasprefetched into the cache 208.

After adjusting the trigger track 302 and/or the prestage amount 304,the machine learning module 212 is retrained (e.g., backwardpropagation) (at block 612) with input 214 comprising currentoperational parameters of prestage information 300 (current triggertrack 302 and prestage amount 304) and I/O activity information 400(e.g., I/O speed of a storage device 402 on which the demoted trackresides; adaptor bandwidth 404 through which data is transferred betweenthe cache 208 and the storage device 112 _(i); a number of allocateddestage tasks 406 and stage tasks 408 to a storage unit (storage deviceor RAID rank)) to produce the adjusted trigger track and adjustedprestage amount if both are adjusted, or produce the adjusted triggertrack and the current prestage amount if only the trigger track isadjusted, or produce the current trigger track and the adjusted prestageamount if only the prestage amount is adjusted. In this way, the machinelearning module 212 is retrained to produce the determined optimizedtrigger track and/or prestage amount based on current operatingconditions reflected by the prestage information 300 and I/O activityinformation 400 provided as input 214 to the machine learning module212.

With the embodiment of FIG. 6 , the machine learning module 212 isretrained to produce an adjusted trigger track and/or adjusted prestagedamount that was adjusted to keep the prefetched tracks in cache 208longer or shorter to ensure that prestaged tracks are available in cache208 when requested to improve the cache hit ratio in the computersystem.

FIG. 7 illustrates an embodiment of operations performed by the I/Omanager 206 and/or machine learning module 212 to retrain the machinelearning module 212 to improve the calculation of the trigger track 216and prestage amount 218 to optimize computer performance in response todetecting a cache miss, a request for a track that is not in the cache208 and needs to be staged from the storage 110 into the cache 208. Theoperations of FIG. 7 may be performed upon each cache miss or after apredetermined number of cache misses occur. Upon detecting (at block700) a cache miss, the I/O manager 206 initiates a stage (at block 702)of the track into cache 208. If (at block 704) the track is not subjectto a sequential access to multiple tracks, then control ends. Otherwise,if (at block 704) the track is subject to sequential access, then theI/O manager 206 calculates (at block 706) a track position margin oferror comprising a (current trigger track minus the requested tracknumber) divided by the number of tracks in the group of consecutivetracks. This calculated track position margin of error indicates how farthe requested track was from the trigger track in the track group whenthe cache miss occurred, i.e., how far the requested track was away frombeing prestaged into the cache 208 where it would have been available toavoid the cache miss.

The I/O manager 206 decreases the trigger track 302 by the trackposition margin of error to produce an adjusted trigger track and/orincreases the prestage amount by the track position margin of error.This adjustment causes tracks to be prefetched sooner and/or more tracksto be prefetched to avoid future occurrences of cache misses. In anembodiment of block 708, the decreasing the trigger track may comprisemultiplying the trigger track by one minus the track position margin oferror and the increasing the prestage amount may comprise multiplyingthe prestage amount by one plus the track position margin of error. Inalternative embodiments, the track position margin of error may be usedin other functions to decrease the trigger track and increase thedestage amount. In alternative embodiments, the track position margin oferror may be calculated in additional ways using the current triggertrack and the requested track number in the stage group.

After adjusting the trigger track 302 and/or the prestage amount 304,the machine learning module 212 is retrained (e.g., backwardpropagation) (at block 710) with input 214 comprising prestageinformation 300 (current trigger track 302 and prestage amount 304) andI/O activity information 400 (e.g., I/O speed of a storage device 402 onwhich the requested track resides; adaptor bandwidth through which datais transferred between the cache 208 and the storage device 112 _(i); anumber of allocated destage tasks 406 and stage tasks 508 to a storageunit (storage device or RAID rank)) to produce the adjusted triggertrack and adjusted prestage amount if both are adjusted, or produce theadjusted trigger track and the current prestage amount if only thetrigger track is adjusted, or produce the current trigger track and theadjusted prestage amount if only the prestage amount is adjusted.

With the embodiment of FIG. 7 , the machine learning module 212 isretrained to produce the determined optimized trigger track and/orprestage amount based on current operating conditions reflected by theprestage information 300 and I/O activity information 400 provided asinput 214 to the machine learning module 212 in order to avoid cachemisses.

The described embodiments provide a dynamic technique for determiningthe trigger track and prestage amounts to use to determine when and howmany tracks to prestage for future access requests to the tracks and adynamic technique to determine when to retrain a machine learning moduleused to calculate the trigger track and prestage amount to use thatoptimizes on one or more storage performance parameters.

In the described embodiment, variables i, j, m, n, etc., when used withdifferent elements may denote a same or different instance of thatelement.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The computational components of FIGS. 1 and 2 , including the storagecontroller 100, host 108, and processing nodes 200 ₁, 200 ₂, 200 _(i)may be implemented in one or more computer systems, such as the computersystem 802 shown in FIG. 8 . Computer system/server 802 may be describedin the general context of computer system executable instructions, suchas program modules, being executed by a computer system. Generally,program modules may include routines, programs, objects, components,logic, data structures, and so on that perform particular tasks orimplement particular abstract data types. Computer system/server 802 maybe practiced in distributed cloud computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed cloud computing environment,program modules may be located in both local and remote computer systemstorage media including memory storage devices.

As shown in FIG. 8 , the computer system/server 802 is shown in the formof a general-purpose computing device. The components of computersystem/server 802 may include, but are not limited to, one or moreprocessors or processing units 804, a system memory 806, and a bus 808that couples various system components including system memory 806 toprocessor 804. Bus 808 represents one or more of any of several types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, andnot limitation, such architectures include Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnects (PCI) bus.

Computer system/server 802 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 802, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 806 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 810 and/or cachememory 812.

Computer system/server 802 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 813 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 808 by one or more datamedia interfaces. As will be further depicted and described below,memory 806 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 814, having a set (at least one) of program modules 816,may be stored in memory 806 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. The components of the computer 802 may be implemented asprogram modules 816 which generally carry out the functions and/ormethodologies of embodiments of the invention as described herein. Thesystems of FIG. 1 may be implemented in one or more computer systems802, where if they are implemented in multiple computer systems 802,then the computer systems may communicate over a network.

Computer system/server 802 may also communicate with one or moreexternal devices 818 such as a keyboard, a pointing device, a display820, etc.; one or more devices that enable a user to interact withcomputer system/server 802; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 802 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 822. Still yet, computer system/server 802can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 824. As depicted, network adapter 824communicates with the other components of computer system/server 802 viabus 808. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 802. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the present invention(s)” unless expressly specifiedotherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or moreintermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the presentinvention.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the present inventionneed not include the device itself.

The foregoing description of various embodiments of the invention hasbeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseform disclosed. Many modifications and variations are possible in lightof the above teaching. It is intended that the scope of the invention belimited not by this detailed description, but rather by the claimsappended hereto. The above specification, examples and data provide acomplete description of the manufacture and use of the composition ofthe invention. Since many embodiments of the invention can be madewithout departing from the spirit and scope of the invention, theinvention resides in the claims herein after appended.

What is claimed is:
 1. A computer program product for determining tracks to prestage into cache from a storage, wherein the computer program product comprises a non-transitory computer readable storage medium having computer readable program code embodied therein that when executed performs operations, the operations comprising: providing input information related to determining sequentially accessed tracks to prestage from the storage to the cache and Input/Output (I/O) activity information to a machine learning module; receiving, from the machine learning module having processed the provided information, a trigger track at which to start prestaging a prestage amount of sequentially accessed tracks in response to an access request to the trigger track; calculating a margin of error based on information related to a sequentially accessed track in the cache; adjusting at least one of the trigger track and the prestage amount by the calculated margin of error to produce at least one of an adjusted trigger track and an adjusted prestage amount; retraining the machine learning module with the input information related to determining sequentially accessed tracks to prestage to produce at least one of the adjusted trigger track and the adjusted prestage amount; and using the retrained machine learning module to produce at least one of a new trigger track and a new prestage amount to use to determine sequentially accessed tracks to prestage.
 2. The computer program product of claim 1, wherein the calculating the margin of error comprises: calculating a time margin of error based on a difference of a current time and a time when the sequentially accessed track was prestaged, wherein the adjusting the at least one of the trigger track and the prestage amount comprises adjusting the at least one of the trigger track and the prestage amount by the time margin of error.
 3. The computer program product of claim 2, wherein the calculating the time margin of error comprises dividing the difference of the current time and the time when the sequentially accessed track was prestaged by an average time tracks are in cache.
 4. The computer program product of claim 2, wherein the operations comprise: demoting a sequentially accessed track from the cache, wherein the sequentially accessed track for which the time margin of error is calculated comprises the demoted sequentially accessed track, wherein the time margin of error is calculated in response to demoting the sequentially accessed track.
 5. The computer program product of claim 4, wherein the operations further comprises: determining whether the demoted sequentially accessed track was accessed while in the cache, wherein the adjusting the at least one of the trigger track and the prestage amount comprises: performing at least one of decreasing the trigger track and increasing the prestage amount by a percentage based on the time margin of error in response to determining that the demoted sequentially accessed track was accessed while in the cache; and performing at least one of increasing the trigger track and decreasing the prestage amount by a percentage based on the time margin of error in response to determining that the demoted sequentially accessed track was not accessed while in the cache.
 6. The computer program product of claim 1, wherein the calculating the margin of error comprises: calculating a track position margin of error based on a difference of the trigger track and a number of the sequentially accessed track, wherein the adjusting the at least one of the trigger track and the prestage amount comprises decreasing the trigger track by the track position margin of error.
 7. The computer program product of claim 6, wherein the operations further comprise: detecting a cache miss to a sequentially accessed track in the cache, wherein the sequentially accessed track for which the track position margin of error is calculated comprises the sequentially accessed track for which the cache miss was detected, wherein the track position margin in of error is calculated in response to detecting the cache miss.
 8. The computer program product of claim 6, wherein the adjusting the at least one of the trigger track and the prestage amount comprises: performing at least one of decreasing the trigger track and increasing the prestage amount by the track position margin of error.
 9. A system for determining tracks to prestage into cache from a storage, comprising: a processor; and a computer readable storage medium having computer readable program code embodied therein that when executed by the processor performs operations, the operations comprising: providing input information related to determining sequentially accessed tracks to prestage from the storage to the cache and Input/Output (I/O) activity information to a machine learning module; receiving, from the machine learning module having processed the provided information, a trigger track at which to start prestaging a prestage amount of sequentially accessed tracks in response to an access request to the trigger track; calculating a margin of error based on information related to a sequentially accessed track in the cache; adjusting at least one of the trigger track and the prestage amount by the calculated margin of error to produce at least one of an adjusted trigger track and an adjusted prestage amount; retraining the machine learning module with the input information related to determining sequentially accessed tracks to prestage to produce at least one of the adjusted trigger track and the adjusted prestage amount; and using the retrained machine learning module to produce at least one of a new trigger track and a new prestage amount to use to determine sequentially accessed tracks to prestage.
 10. The system of claim 9, wherein the calculating the margin of error comprises: calculating a time margin of error based on a difference of a current time and a time when the sequentially accessed track was prestaged, wherein the adjusting the at least one of the trigger track and the prestage amount comprises adjusting the at least one of the trigger track and the prestage amount by the time margin of error.
 11. The system of claim 10, wherein the calculating the time margin of error comprises dividing the difference of the current time and the time when the sequentially accessed track was prestaged by an average time tracks are in cache.
 12. The system of claim 10, wherein the operations comprise: demoting a sequentially accessed track from the cache, wherein the sequentially accessed track for which the time margin of error is calculated comprises the demoted sequentially accessed track, wherein the time margin of error is calculated in response to demoting the sequentially accessed track.
 13. The system of claim 12, wherein the operations further comprises: determining whether the demoted sequentially accessed track was accessed while in the cache, wherein the adjusting the at least one of the trigger track and the prestage amount comprises: performing at least one of decreasing the trigger track and increasing the prestage amount by a percentage based on the time margin of error in response to determining that the demoted sequentially accessed track was accessed while in the cache; and performing at least one of increasing the trigger track and decreasing the prestage amount by a percentage based on the time margin of error in response to determining that the demoted sequentially accessed track was not accessed while in the cache.
 14. The system of claim 9, wherein the calculating the margin of error comprises: calculating a track position margin of error based on a difference of the trigger track and a number of the sequentially accessed track, wherein the adjusting the at least one of the trigger track and the prestage amount comprises decreasing the trigger track by the track position margin of error.
 15. The system of claim 14, wherein the operations further comprise: detecting a cache miss to a sequentially accessed track in the cache, wherein the sequentially accessed track for which the track position margin of error is calculated comprises the sequentially accessed track for which the cache miss was detected, wherein the track position margin in of error is calculated in response to detecting the cache miss.
 16. The system of claim 14, wherein the adjusting the at least one of the trigger track and the prestage amount comprises: performing at least one of decreasing the trigger track and increasing the prestage amount by the track position margin of error.
 17. A method for determining tracks to prestage into cache from a storage, comprising: providing input information related to determining sequentially accessed tracks to prestage from the storage to the cache and Input/Output (I/O) activity information to a machine learning module; receiving, from the machine learning module having processed the provided information, a trigger track at which to start prestaging a prestage amount of sequentially accessed tracks in response to an access request to the trigger track; calculating a margin of error based on information related to a sequentially accessed track in the cache; adjusting at least one of the trigger track and the prestage amount by the calculated margin of error to produce at least one of an adjusted trigger track and an adjusted prestage amount; retraining the machine learning module with the input information related to determining sequentially accessed tracks to prestage to produce at least one of the adjusted trigger track and the adjusted prestage amount; and using the retrained machine learning module to produce at least one of a new trigger track and a new prestage amount to use to determine sequentially accessed tracks to prestage.
 18. The method of claim 17, wherein the calculating the margin of error comprises: calculating a time margin of error based on a difference of a current time and a time when the sequentially accessed track was prestaged, wherein the adjusting the at least one of the trigger track and the prestage amount comprises adjusting the at least one of the trigger track and the prestage amount by the time margin of error.
 19. The method of claim 18, wherein the calculating the time margin of error comprises dividing the difference of the current time and the time when the sequentially accessed track was prestaged by an average time tracks are in cache.
 20. The method of claim 18, further comprising: demoting a sequentially accessed track from the cache, wherein the sequentially accessed track for which the time margin of error is calculated comprises the demoted sequentially accessed track, wherein the time margin of error is calculated in response to demoting the sequentially accessed track. 